Pages

Monday, May 7, 2018

Gene Mapping Lays Groundwork for Precision Chemotherapy

UCSF: Despite the great successes of targeted cancer drugs and the promise
of novel immunotherapies, the vast majority of people diagnosed with
cancer are still first treated with chemotherapy. Now a new study by
UCSF researchers using techniques drawn from computational biology could
make it much easier for physicians to use the genetic profile of a
patient’s tumor to pick the chemotherapy treatment with the fewest side
effects and best chance of success. “Since 95 percent of cancer patients still get chemo, we realized we
could make a major impact on cancer treatment by helping clinicians
prescribe the right chemotherapy drug,” said Sourav Bandyopadhyay, PhD, a professor of bioengineering and therapeutic sciences in UCSF’s Schools of Pharmacy and Medicine and senior author on the new study.

Optimizing Drugs Based on Tumor Genetics

Chemotherapies are potent toxins delivered into the bloodstream to
kill tumor cells throughout the body by damaging DNA in rapidly dividing
cells. However, these poisons can also do significant harm to other
dividing cells such as those found in the stomach lining and in hair and
nail follicles, as well as the blood and immune stem cells in the bone
marrow. In addition, cancer cells’ susceptibility to these agents varies
widely, and tumors often develop resistance to drugs that initially
seem effective.
There are more than 100 chemotherapy agents in wide use, but
oncologists have very little information to guide their decisions about
which of these drugs to use in a given patient. These decisions are
typically guided by the drugs’ average historical success rate for
different types of cancer, rather than any understanding of how the
chemotherapy drug will interact with the genetic profile of a specific
tumor.
“We know very little about how gene mutations in tumor cells can
change how a tumor might respond or not to certain chemotherapy drugs.
Mapping these sorts of connections could make it possible to optimize
which drugs patients get based on their tumor genetics,” said
Bandyopadhyay, a member of the UCSF Helen Diller Family Comprehensive
Cancer Center and the Quantitative Biosciences Institute.
Now — in a paper published online April 17, 2018 in Cell Reports
— Bandyopadhyay’s lab has systematically mapped connections between 625
breast and ovarian cancer genes and nearly every FDA-approved
chemotherapy for breast or ovarian cancer. Led by Hsien-Ming “Kevin” Hu,
PhD, Bandyopadhyay’s group developed a high-throughput combinatorial
approach that allowed them to perform 80,000 experiments in laboratory
dishes in a matter of weeks. The authors said their results, which they
have made publicly available, constitute an invaluable resource to help
clinicians predict which chemotherapies will be most effective against
tumor cells with particular genetic mutations, and how to rationally
combine therapies to prevent cancers from developing resistance.Sourav Bandyopadhyay, PhD, a professor of bioengineering and therapeutic sciences and senior author of the study.“We’re
trying to take a systems view of chemotherapy resistance,”
Bandyopadhyay said. “With rarer mutations in particular there aren’t
enough patients for large clinical trials to be able to identify
biomarkers of resistance, but by considering all the different potential
genetic factors that have been identified together in one study, we can
robustly predict from experiments in laboratory dishes how cancers with
different genetic mutations will respond to different treatments.”

Predicting Response to Chemotherapy

The team began by identifying hundreds of genes frequently mutated in
human cancers: 200 implicated in breast cancer, 170 linked to ovarian
cancer, and 134 involved in DNA repair, which is compromised in many
types of cancer. They then mimicked the effects of such mutations in lab
dishes by systematically inactivating each of these cancer-associated
genes in healthy human cells, creating 625 different perturbations that
mirrored distinct genetic mutations seen in real breast and ovarian
cancers.
The researchers then exposed cells from each of these lines to a
panel of 31 different drug treatments — including 23 chemotherapy
compounds approved by the FDA for breast and ovarian cancers, six
targeted cancer drugs, and two common drug combinations. An automated
microscopy system monitored the cells’ health and recorded which groups
of cells were killed, which survived, and which developed resistance
when exposed to a particular treatment.
The resulting “map” of gene-drug interactions allowed the researchers
to accurately predict the responses of multiple human cancer cell lines
to different chemotherapy agents based on the cell lines’ genetic
profiles and also revealed new genetic factors that appear to determine
the response of breast and ovarian tumor cells to common classes of
chemotherapy treatment.

Clinical Trial for Ovarian Cancer

As a proof of principle, the researchers collaborated with Clovis
Oncology, a biotech company based in Boulder, Colorado, which is running
a clinical trial of drugs known as PARP inhibitors in patients with
stage II ovarian cancer. Based on their gene–drug interaction map, the
researchers predicted that mutations in two genes, called ARID1A and GPBP1,
could contribute to ovarian cancer’s ability to develop resistance to
this class of drugs. Results from the clinical trial bore out these
predictions: patients with these mutations were significantly more
likely to develop resistance.
Bandyopadhyay’s team has deposited the trove of data generated in the
new study in a database maintained by the National Cancer Institute so
that other researchers can mine it for information about drug
combinations and derive new biological insights about the basis for
chemotherapy’s success or failure. The lab is also working with the
Breast Oncology Program at UCSF to make this data part of an adaptive
clinical trial called I-SPY,
which lets researchers identify the most effective therapies based on
patient molecular profiling, and is collaborating with members of the UCSF Institute for Computational Health Sciences
(ICHS) to put these and other public data into a centralized database
that clinicians can access through an app to help make the most
appropriate treatment decisions.
In the future, Bandyopadhyay says, better understanding how
chemotherapy agents impact specific biological pathways should allow
drug trials to focus on patients who are more likely to respond to the
drugs being tested and enable clinicians to identify targeted or
combination therapies for patients with a genetic predisposition to
resistance.
Other authors on the study included Xin Zhao, PhD, Swati Kaushik, PhD, Antoine Barthelet, Kevin K. Lin, PhD, and Khyati N. Shah,
PhD, of UCSF; and Lilliane Robillard, PhD, Andy D. Simmons, PhD, Mitch
Raponi, PhD, and Thomas C. Harding, PhD, of Clovis Oncology.
The study was funded by the NCI (U01CA168370), the UCSF Program in
Breakthrough Biomedical Research (PBBR) and the UCSF Breast Oncology
SPORE development award.Conflict of Interest Statement: Co-authors Robillard, Simmons, Raponi, and Harding are employees of Clovis Oncology.